Face recognition technology is becoming widely used in access control, attendance systems, authentication, and security solutions. Recently, I worked on building a real-world face recognition system using Python + Flask, focusing on accuracy and speed.
Here is the core idea of the system:
1. User Registration
Users can register with photos (base64 or folder-based). The system extracts:
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Full face encodings
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Eye + forehead region encodings
These are saved in.binformat along with metadata in JSON.
2. Recognition Process
The API receives an image stream, detects the face, extracts encodings, and matches them with stored data even if the user has:
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Beard or no beard
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Slight face angle differences
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Low lighting
3. Optimized Matching
The system uses threshold-based distance comparison instead of heavy models, making the API very fast.
4. Real-World Use Cases
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Attendance systems
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Touchless entry
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Visitor verification
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Digital identity onboarding
This project taught me how small optimizations can drastically improve performance. I will share code examples and sample APIs soon — stay tuned!